Baseline MRI Examination in the NAKO Health Study­Findings on Feasibility, Participation and Dropout Rates, Comfort, and Image Quality.

BACKGROUND: Magnetic resonance imaging (MRI) yields important information on the development and current status of many different diseases. Whole-body MRI was accordingly made a part of the multicenter, population-based NAKO Health Study. The present analysis concerns the feasibility of the baseline MRI examination and various aspects of quality assurance over the period 2014-2019. METHODS: 32 252 participants in the NAKO Health Study, aged 20 to 74, who had no contraindication to MRI were invited to undergo scanning in one of five MRI study centers across Germany. The whole-body MRI scan took about one hour and consisted of sequences for the visualization of structural and functional features of the brain, musculoskeletal system, cardiovascular system, and thoracoabdominal system. A comprehensive quality-assurance assessment was carried out, with evaluation of adverse events, the completeness of the MRI protocols, the participants' subjective perceptions, and image quality. RESULTS: 31 578 participants (97.9%) were successfully included in the MRI study. They reported a high level of comfort and suffered no severe adverse events; mild adverse events occurred in only four participants. Depending on the imaging sequence, the image quality was rated as excellent in 80.2% to 96.8% of cases. Quality assessment with respect to structural features of the brain revealed high consistency across study centers, as well as with regard to age-and sex-based differences in brain volume (men, 1203.81 ± 102.06 cm³; women, 1068.10 ± 86.69 cm³). CONCLUSION: Whole-body MRI was successfully implemented in the NAKO baseline examination and was associated with high patient comfort and very good image quality. The imaging biomarkers of the brain confirmed previously observed differences based on age and sex, underscoring the feasibility of data pooling.

GOLD-grade specific disease characterization and phenotyping of COPD using quantitative computed tomography in the nationwide COSYCONET multicenter trial in Germany.

INTRODUCTION: To apply quantitative computed tomography (QCT) for GOLD-grade specific disease characterization and phenotyping of air-trapping, emphysema, and airway abnormalities in patients with chronic obstructive pulmonary disease (COPD) from a nationwide cohort study. METHODS: As part of the COSYCONET multicenter study, standardized CT in ex- and inspiration, lung function assessment (FEV1/FVC) and clinical scores (BODE index) were prospectively acquired in 525 patients (192women, 327men, aged 65.7±8.5y) at risk for COPD and at GOLD1-4. QCT parameters total lung volume (TLV), emphysema index (EI), parametric response mapping (PRM) for emphysema (PRMEmph) and functional small airway disease (PRMfSAD), total airway volume (TAV), wall percentage (WP) and total diameter (TD) were computed using automated software. RESULTS: TLV, EI, PRMfSAD and PRMEmph increased incrementally with each GOLD grade (p<0.001). Aggregated WP5-10 of subsegmental airways was higher from GOLD1 to GOLD3 and lower again at GOLD4 (p<0.001), whereas TD5-10 was significantly dilated only in GOLD4 (p<0.001). 58 patients were phenotyped as 'non-airway non-emphysema type', 202 as 'airway type', 96 as 'emphysema type' and 169 as 'mixed type'. FEV1/FVC was best in 'non-airway non-emphysema type' compared to other phenotypes, while 'mixed type' had worst FEV1/FVC (p<0.001). BODE index was 0.56±0.72 in the 'non-airway non-emphysema type' and highest with 2.55±1.77 in 'mixed type' (p<0.001). CONCLUSION: QCT demonstrates increasing hyperinflation and emphysema dependent on GOLD grade, while airway wall thickening increases until GOLD 3 and airway dilatation occurs in GOLD4. QCT identifies four disease phenotypes with implications for lung function and prognosis.

Performance of different CT enhancement quantification methods as predictors of pancreatic cancer recurrence after upfront surgery.

The prognosis of pancreatic cancer (PDAC) after tumor resection remains poor, mostly due to a high but variable risk of recurrence. A promising tool for improved prognostication is the quantification of CT tumor enhancement. For this, various enhancement formulas have been used in previous studies. However, a systematic comparison of these formulas is lacking. In the present study, we applied twenty-three previously published CT enhancement formulas to our cohort of 92 PDAC patients who underwent upfront surgery. We identified seven formulas that could reliably predict tumor recurrence. Using these formulas, weak tumor enhancement was associated with tumor recurrence at one and two years after surgery (p ≤ 0.030). Enhancement was inversely associated with adverse clinicopathological features. Low enhancement values were predictive of a high recurrence risk (Hazard Ratio ≥ 1.659, p ≤ 0.028, Cox regression) and a short time to recurrence (TTR) (p ≤ 0.027, log-rank test). Some formulas were independent predictors of TTR in multivariate models. Strikingly, almost all of the best-performing formulas measure solely tumor tissue, suggesting that normalization to non-tumor structures might be unnecessary. Among the top performers were also the absolute arterial/portal venous tumor attenuation values. These can be easily implemented in clinical practice for better recurrence prediction, thus potentially improving patient management.

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer.

OBJECTIVE: Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features. METHODS: This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS). RESULTS: Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival. CONCLUSION: Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early. CRITICAL RELEVANCE STATEMENT: Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging. KEY POINTS: Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

Calcium-Based Imaging of the Spine at Dual-Layer CT and Evaluation of Vertebral Fractures in Multiple Myeloma.

PURPOSE: To evaluate the prediction of vertebral fractures in plasma cell dyscrasias using dual-layer CT (DLCT) with quantitative assessment of conventional CT image data (CI), calcium suppressed image data (CaSupp), and calculation of virtual calcium-only (VCa) image data. MATERIAL AND METHODS: Patients (n = 81) with the diagnosis of a plasma cell dyscrasia and whole-body DLCT at the time of diagnosis and follow-up were retrospectively enrolled. CI, CaSupp25, and CaSupp100 were quantitatively analyzed using regions of interest in the lumbar vertebral bodies and fractured vertebral bodies on baseline or follow-up imaging. VCa were calculated by subtraction (CaSupp100-CaSupp25), delineating bone only. Logistic regression analyses were performed to assess the possibility of imminent spine fractures. RESULTS: In 24 patients, new vertebral fractures were observed in the follow-up imaging. The possibility of new vertebral fractures was significant for baseline assessment of CT numbers in CI, CaSupp25, and VCa (p = 0.01, respectively), with a higher risk for new fractures in the case of lower CT numbers in CI (Odds ratio = [0.969; 0.994]) and VCa (Odds ratio = [0.978; 0.995]) and in the case of higher CT numbers in CaSupp 25 (Odds ratio 1.015 [1.006; 1.026]). Direct model comparisons implied that CT numbers in CaSupp 25 and VCa might show better fracture prediction than those in CI (R2 = 0.18 both vs. 0.15; AICc = 91.95, 91.79 vs. 93.62), suggesting cut-off values for CI at 103 HU (sensitivity: 54.2%; specificity: 82.5; AUC: 0.69), for VCa at 129 HU (sensitivity: 41.7%; specificity: 94.7; AUC: 0.72). CONCLUSIONS: Quantitative assessment with CaSupp and calculation of VCa is feasible to predict the vertebral fracture risk in MM patients. DLCT may prove useful in detecting imminent fractures.

How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection.

OBJECTIVES: To evaluate the performance and potential biases of deep-learning models in detecting chronic obstructive pulmonary disease (COPD) on chest CT scans across different ethnic groups, specifically non-Hispanic White (NHW) and African American (AA) populations. MATERIALS AND METHODS: Inspiratory chest CT and clinical data from 7549 Genetic epidemiology of COPD individuals (mean age 62 years old, 56-69 interquartile range), including 5240 NHW and 2309 AA individuals, were retrospectively analyzed. Several factors influencing COPD binary classification performance on different ethnic populations were examined: (1) effects of training population: NHW-only, AA-only, balanced set (half NHW, half AA) and the entire set (NHW + AA all); (2) learning strategy: three supervised learning (SL) vs. three self-supervised learning (SSL) methods. Distribution shifts across ethnicity were further assessed for the top-performing methods. RESULTS: The learning strategy significantly influenced model performance, with SSL methods achieving higher performances compared to SL methods (p < 0.001), across all training configurations. Training on balanced datasets containing NHW and AA individuals resulted in improved model performance compared to population-specific datasets. Distribution shifts were found between ethnicities for the same health status, particularly when models were trained on nearest-neighbor contrastive SSL. Training on a balanced dataset resulted in fewer distribution shifts across ethnicity and health status, highlighting its efficacy in reducing biases. CONCLUSION: Our findings demonstrate that utilizing SSL methods and training on large and balanced datasets can enhance COPD detection model performance and reduce biases across diverse ethnic populations. These findings emphasize the importance of equitable AI-driven healthcare solutions for COPD diagnosis. CRITICAL RELEVANCE STATEMENT: Self-supervised learning coupled with balanced datasets significantly improves COPD detection model performance, addressing biases across diverse ethnic populations and emphasizing the crucial role of equitable AI-driven healthcare solutions. KEY POINTS: Self-supervised learning methods outperform supervised learning methods, showing higher AUC values (p < 0.001). Balanced datasets with non-Hispanic White and African American individuals improve model performance. Training on diverse datasets enhances COPD detection accuracy. Ethnically diverse datasets reduce bias in COPD detection models. SimCLR models mitigate biases in COPD detection across ethnicities.

Midregional Proatrial Natriuretic Peptide (MRproANP) is associated with vertebral fractures and low bone density in patients with chronic obstructive pulmonary disease (COPD).

BACKGROUND: Patients with COPD are often affected by loss of bone mineral density (BMD) and osteoporotic fractures. Natriuretic peptides (NP) are known as cardiac markers, but have also been linked to fragility-associated fractures in the elderly. As their functions include regulation of fluid and mineral balance, they also might affect bone metabolism, particularly in systemic disorders such as COPD. RESEARCH QUESTION: We investigated the association between NP serum levels, vertebral fractures and BMD assessed by chest computed tomography (CT) in patients with COPD. METHODS: Participants of the COSYCONET cohort with CT scans were included. Mean vertebral bone density on CT (BMD-CT) as a risk factor for osteoporosis was assessed at the level of TH12 (AI-Rad Companion), and vertebral compression fractures were visually quantified by two readers. Their relationship with N-terminal pro-B-type natriuretic peptide (NT-proBNP), Mid-regional pro-atrial natriuretic peptide (MRproANP) and Midregional pro-adrenomedullin (MRproADM) was determined using group comparisons and multivariable analyses. RESULTS: Among 418 participants (58% male, median age 64 years, FEV1 59.6% predicted), vertebral fractures in TH12 were found in 76 patients (18.1%). Compared to patients without fractures, these had elevated serum levels (p ≤ 0.005) of MRproANP and MRproADM. Using optimal cut-off values in multiple logistic regression analyses, MRproANP levels ≥ 65 nmol/l (OR 2.34; p = 0.011) and age (p = 0.009) were the only significant predictors of fractures after adjustment for sex, BMI, smoking status, FEV1% predicted, SGRQ Activity score, daily physical activity, oral corticosteroids, the diagnosis of cardiac disease, and renal impairment. Correspondingly, MRproANP (p < 0.001), age (p = 0.055), SGRQ Activity score (p = 0.061) and active smoking (p = 0.025) were associated with TH12 vertebral density. INTERPRETATION: MRproANP was a marker for osteoporotic vertebral fractures in our COPD patients from the COSYCONET cohort. Its association with reduced vertebral BMD on CT and its known modulating effects on fluid and ion balance are suggestive of direct effects on bone mineralization. TRIAL REGISTRATION: ClinicalTrials.gov NCT01245933, Date of registration: 18 November 2010.

Non-contrast free-breathing liver perfusion imaging using velocity selective ASL combined with prospective motion compensation.

PURPOSE: To apply velocity selective arterial spin labeling (VSASL) combined with a navigator-based (NAV) prospective motion compensation method for a free-breathing liver perfusion measurement without contrast agent. METHODS: Sinc-modulated Velocity Selective Inversion (sinc-VSI) pulses were applied as labeling and control pulses. In order to account for respiratory motion, a navigator was employed in the form of a single gradient-echo projection readout, located at the diaphragm along the inferior-superior direction. Prior to each transverse imaging slice of the spin-echo EPI based readouts, navigator and fat suppression were incorporated. Motion data was obtained from the navigator and transmitted back to the sequence, allowing real-time adjustments to slice positioning. The sinc-VSI without velocity-selective gradients during the control condition but with velocity-selective gradients along all three directions during labeling was chosen for the VSASL. The VSASL was compared with pseudo-continuous ASL (pCASL) methods, which selectively tagged the moving spins using a tagging plane placed at the portal vein and hepatic artery. RESULTS: The motion caused by respiratory activity was effectively computed using the navigator signal. The coefficients of variation (CoV) of average liver voxel in NAV were significantly decreased when compared to breath-hold (BH), with an average reduction of 29.4 ±â€¯18.44% for control images, and 29.89 ±â€¯20.83% for label images (p < 0.001). The resulting maps of normalized ASL signal (normalized to M0) showed significantly higher perfusion weightings in the NAV-compensated VSASL, when compared to the NAV-compensated pCASL techniques. CONCLUSIONS: This study demonstrates the feasibility of using a navigator-based prospective motion compensation technique in conjunction with VSASL for the measurement of liver perfusion without the use of contrast agents while allowing for free-breathing.

Expiratory Venous Volume and Arterial Tortuosity are Associated with Disease Severity and Mortality Risk in Patients with COPD: Results from COSYCONET.

Purpose: The aim of this study was to evaluate the association between computed tomography (CT) quantitative pulmonary vessel morphology and lung function, disease severity, and mortality risk in patients with chronic obstructive pulmonary disease (COPD). Patients and Methods: Participants of the prospective nationwide COSYCONET cohort study with paired inspiratory-expiratory CT were included. Fully automatic software, developed in-house, segmented arterial and venous pulmonary vessels and quantified volume and tortuosity on inspiratory and expiratory scans. The association between vessel volume normalised to lung volume and tortuosity versus lung function (forced expiratory volume in 1 sec [FEV1]), air trapping (residual volume to total lung capacity ratio [RV/TLC]), transfer factor for carbon monoxide (TLCO), disease severity in terms of Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D, and mortality were analysed by linear, logistic or Cox proportional hazard regression. Results: Complete data were available from 138 patients (39% female, mean age 65 years). FEV1, RV/TLC and TLCO, all as % predicted, were significantly (p < 0.05 each) associated with expiratory vessel characteristics, predominantly venous volume and arterial tortuosity. Associations with inspiratory vessel characteristics were absent or negligible. The patterns were similar for relationships between GOLD D and mortality with vessel characteristics. Expiratory venous volume was an independent predictor of mortality, in addition to FEV1. Conclusion: By using automated software in patients with COPD, clinically relevant information on pulmonary vasculature can be extracted from expiratory CT scans (although not inspiratory scans); in particular, expiratory pulmonary venous volume predicted mortality. Trial Registration: NCT01245933.

CT-guided needle biopsy is not associated with increased ipsilateral pleural metastasis.

INTRODUCTION: Histological confirmation of a lung tumor is the prerequisite for treatment planning. It has been suspected that CT-guided needle biopsy (CTGNB) exposes the patient to a higher risk of pleural recurrence. However, the distance between tumor and pleura has largely been neglected as a possible confounder when comparing CTGNB to bronchoscopy. METHODS: All patients with lung cancer histologically confirmed by bronchoscopy or CTGNB between 2010 and 2020 were enrolled and studied. Patients' medical histories, radiologic and pathologic findings and surgical records were reviewed. Pleural recurrence was diagnosed by pleural biopsy, fluid cytology, or by CT chest imaging showing progressive pleural nodules. RESULTS: In this retrospective unicenter analysis, 844 patients underwent curative resection for early-stage lung cancer between 2010 and 2020. Median follow-up was 47.5 months (3-137). 27 patients (3.2 %) with ipsilateral pleural recurrence (IPR) were identified. The distance of the tumor to the pleura was significantly smaller in patients who underwent CTGNB. A tendency of increased risk of IPR was observed in tumors located in the lower lobe (HR: 2.18 [±0.43], p = 0.068), but only microscopic pleural invasion was a significant independent predictive factor for increased risk of IPR (HR: 5.33 [± 0.51], p = 0.001) by multivariate cox analysis. Biopsy by CTGNB did not affect IPR (HR: 1.298 [± 0.39], p = 0.504). CONCLUSION: CTGNB is safe and not associated with an increased incidence of IPR in our cohort of patients. This observation remains to be validated in a larger multicenter patient cohort.

Improvement of Breast Cancer Detection Using Dual-Layer Spectral CT.

This study aimed to investigate the diagnostic performance of breast mass detection on monoenergetic image data at 40 keV (MonoE40) and on iodine maps (IM) compared with conventional image data (CI). In this prospective single-center case-control study, 50 breast cancer patients were examined using contrast-enhanced dual-layer spectral CT. For qualitative and quantitative comparison of MonoE40 and IM with CI image data, four blinded, independent readers assessed 300 randomized single slices (two slices for each imaging type per case) with or without cancerous lesions for the presence of a breast mass. Detection sensitivity and specificity were calculated and readers rated their subjective diagnostic certainty. For statistical analysis of sensitivity and specificity, a paired t-test and ANOVA were used (significance level p = 0.05). A total of 50 female patients (median age 51 years, range 28-83 years) participated. IM had the highest overall scores in sensitivity and specificity for breast cancer detection, with 0.97 ± 0.06 and 0.95 ± 0.07, respectively, compared with 0.90 ± 0.04 and 0.92 ± 0.06 in CI. MonoE40 yielded a sensitivity of 0.96 ± 0.02 and specificity of 0.94 ± 0.08. All differences in sensitivity and specificity between MonoE or IM and CI were statistically significant (p < 0.001). The superiority of IM sensitivity and specificity was most pronounced in patients with dense breasts. Spectral CT improved the detection of breast cancer with higher sensitivity and specificity compared to conventional image data in our study.

Predicting nodal metastasis progression of oral tongue cancer using a hidden Markov model in MRI.

Objectives: The presence of occult nodal metastases in patients with oral tongue squamous cell carcinomas (OTSCCs) has implications for treatment. More than 30% of patients will have occult nodal metastases, yet a considerable number of patients undergo unnecessary invasive neck dissection to confirm nodal status. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify the risk of microscopic involvement at the lymph node level (LNL) given the location of macroscopic metastases and the tumor stage using the MRI method. Materials and methods: A total of 108 patients of OTSCCs were included in the study. A hidden Markov model (HMM) was used to compute the probabilities of transitions between states over time based on MRI. Learning of the transition probabilities was performed via Markov chain Monte Carlo sampling and was based on a dataset of OTSCC patients for whom involvement of individual LNLs was reported. Results: Our model found that the most common involvement was that of level I and level II, corresponding to a high probability of 𝑝b1 = 0.39 ± 0.05, 𝑝b2 = 0.53 ± 0.09; lymph node level I had metastasis, and the probability of metastasis in lymph node II was high (93.79%); lymph node level II had metastasis, and the probability of metastasis in lymph node III was small (7.88%). Lymph nodes progress faster in the early stage and slower in the late stage. Conclusion: An HMM can produce an algorithm that is able to predict nodal metastasis evolution in patients with OTSCCs by analyzing the macroscopic metastases observed in the upstream levels, and tumor category.

MRI-Derived Dural Sac and Lumbar Vertebrae 3D Volumetry Has Potential for Detection of Marfan Syndrome.

PURPOSE: To assess the feasibility and diagnostic accuracy of MRI-derived 3D volumetry of lower lumbar vertebrae and dural sac segments using shape-based machine learning for the detection of Marfan syndrome (MFS) compared with dural sac diameter ratios (the current clinical standard). MATERIALS AND METHODS: The final study sample was 144 patients being evaluated for MFS from 01/2012 to 12/2016, of whom 81 were non-MFS patients (46 [67%] female, 36 ± 16 years) and 63 were MFS patients (36 [57%] female, 35 ± 11 years) according to the 2010 Revised Ghent Nosology. All patients underwent 1.5T MRI with isotropic 1 × 1 × 1 mm3 3D T2-weighted acquisition of the lumbosacral spine. Segmentation and quantification of vertebral bodies L3-L5 and dural sac segments L3-S1 were performed using a shape-based machine learning algorithm. For comparison with the current clinical standard, anteroposterior diameters of vertebral bodies and dural sac were measured. Ratios between dural sac volume/diameter at the respective level and vertebral body volume/diameter were calculated. RESULTS: Three-dimensional volumetry revealed larger dural sac volumes (p < 0.001) and volume ratios (p < 0.001) at L3-S1 levels in MFS patients compared with non-MFS patients. For the detection of MFS, 3D volumetry achieved higher AUCs at L3-S1 levels (0.743, 0.752, 0.808, and 0.824) compared with dural sac diameter ratios (0.673, 0.707, 0.791, and 0.848); a significant difference was observed only for L3 (p < 0.001). CONCLUSION: MRI-derived 3D volumetry of the lumbosacral dural sac and vertebral bodies is a feasible method for quantifying dural ectasia using shape-based machine learning. Non-inferior diagnostic accuracy was observed compared with dural sac diameter ratio (the current clinical standard for MFS detection).

[Differential diagnosis of cystic and nodular lung diseases]. / Differenzialdiagnose zystischer und nodulärer Lungenkrankheiten.

BACKGROUND: Cystic and nodular lung diseases encompass a broad spectrum of diseases with different etiologies and clinicoradiological presentations. Their differentiation is crucial for patient management but can be complex due to diseases with features of both categories and overlapping radiological patterns. OBJECTIVE: This study aims to describe the imaging features of cystic and nodular lung diseases in high-resolution computed tomography (CT) in detail-primarily based on their etiology-in order to allow a more accurate differential diagnosis of these diseases. MATERIALS AND METHODS: A narrative review based on current literature on the topic was conducted from a clinicoradiological perspective. RESULTS: This paper systematically categorizes the differential diagnosis of cystic and nodular lung disease and provides insights into their radiological patterns and etiologies. It highlights the role of CT in the diagnosis of these diseases and emphasizes the importance of multidisciplinary panels combining expertise from radiology, pulmonology, rheumatology, and pathology. CONCLUSION: Reliable differential diagnosis of cystic and nodular lung diseases, particularly based on their radiological features alone, remains difficult due to their overlapping and dynamic nature. Multidisciplinary boards should be the clinical standard for accurate work-up of these diseases, as they combine the medical history, symptoms, radiological findings, and, if necessary, histopathological examinations, thus providing a more robust framework for diagnosis and management.

Imaging differentiation of solid pseudopapillary neoplasms and neuroendocrine neoplasms of the pancreas.

Purpose: The present study aimed to compare the computed tomography (CT) and magnetic resonance imaging (MRI) features of solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (pNENs). Method: Lesion imaging features of 39 patients with SPNs and 127 patients with pNENs were retrospectively extracted from 104 CT and 91 MRI scans. Results: Compared to pNEN patients, SPN patients were significantly younger (mean age 51.8 yrs versus 32.7 yrs) and more often female (female: male ratio, 5.50:1 versus 1.19:1). Most SPNs and pNENs presented as well-defined lesions with an expansive growth pattern. SPNs more often appeared as round or ovoid lesions, compared to pNENs which showed a lobulated or irregular shape in more than half of cases (p<0.01). A surrounding capsule was detected in the majority of SPNs, but only in a minority of pNENs (<0.01). Hemorrhage occurred non-significantly more often in SPNs (p=0.09). Signal inhomogeneity in T1-fat-saturated (p<0.01) and T2-weighted imaging (p=0.046) as well as cystic degeneration (p<0.01) were more often observed in SPNs. Hyperenhancement in the arterial and portal-venous phase was more common in pNENs (p<0.01). Enlargement of locoregional lymph nodes (p<0.01) and liver metastases (p=0.03) were observed in some pNEN patients, but not in SPN patients. Multivariate logistic regression identified the presence of a capsule (p<0.01), absence of arterial hyperenhancement (p<0.01), and low patient age (p<0.01), as independent predictors for SPN. Conclusions: The present study provides three key features for differentiating SPNs from pNENs extracted from a large patient cohort: presence of a capsule, absence of arterial hyperenhancement, and low patient age.

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding.

PURPOSE: Pancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors. METHODS: We analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients). RESULTS: 64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352). CONCLUSION: Our study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.

Multiscale and multimodal imaging for three-dimensional vascular and histomorphological organ structure analysis of the pancreas.

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.

Navigator-based motion compensation for liver BOLD measurement with five-echo SAGE EPI and breath-hold task.

PURPOSE: The purpose of this work is to apply multi-echo spin- and gradient-echo (SAGE) echo-planar imaging (EPI) combined with a navigator-based (NAV) prospective motion compensation method for a quantitative liver blood oxygen level dependent (BOLD) measurement with a breath-hold (BH) task. METHODS: A five-echo SAGE sequence was developed to quantitatively measure T2 and T2* to depict function with sufficient signal-to-noise ratio, spatial resolution and sensitivity to BOLD changes induced by the BH task. To account for respiratory motion, a navigator was employed in the form of a single gradient-echo projection readout, located at the diaphragm along the inferior-superior direction. Prior to each transverse imaging slice of the spin-echo EPI-based readouts, navigator acquisition and fat suppression were incorporated. Motion data was obtained from the navigator and transmitted back to the sequence, allowing real-time adjustments to slice positioning. Six healthy volunteers and three patients with liver carcinoma were included in this study. Quantitative T2 and T2* were calculated at each time point of the BH task. Parameters of t value from first-level analysis using a general linear model and hepatovascular reactivity (HVR) of Echo1, T2 and T2* were calculated. RESULTS: The motion caused by respiratory activity was successfully compensated using the navigator signal. The average changes of T2 and T2* during breath-hold were about 1% and 0.7%, respectively. With the help of NAV prospective motion compensation whole liver t values could be obtained without motion artifacts. The quantified liver T2 (34.7 ± 0.7 ms) and T2* (29 ± 1.2 ms) values agreed with values from literature. In healthy volunteers, the distribution of statistical t value and HVR was homogeneous throughout the whole liver. In patients with liver carcinoma, the distribution of t value and HVR was inhomogeneous due to metastases or therapy. CONCLUSIONS: This study demonstrates the feasibility of using a NAV prospective motion compensation technique in conjunction with five-echo SAGE EPI for the quantitative measurement of liver BOLD with a BH task.

Feasibility of Reduced Iodine Loads for Vascular Assessment Prior to Transcatheter Aortic Valve Implantation (TAVI) Using Spectral Detector CT.

Reduced iodine loads for computed tomography (CT)-based vascular assessment prior to transcatheter aortic valve implantation (TAVI) may be feasible in conjunction with a spectral detector CT scanner. This prospective single-center study considered 100 consecutive patients clinically referred for pre-TAVI CT. They were examined on a dual-layer detector CT scanner to obtain an ECG-gated cardiac scan and a non-ECG-gated aortoiliofemoral scan. Either a standard contrast media (SCM) protocol using 80 mL Iohexol 350 mgI/mL (iodine load: 28 gI) or a body-mass-index adjusted reduced contrast media (RCM) protocol using 40-70 mL Iohexol 350 mgI/mL (iodine load: 14-24.5 gI) were employed. Conventional images and virtual monoenergetic images at 40-80 keV were reconstructed. A threshold of 250 HU was set for sufficient attenuation along the arterial access pathway. A qualitative assessment used a five-point Likert scale. Sufficient attenuation in the thoracic aorta was observed for all patients in both groups using conventional images. In the abdominal, iliac, and femoral segments, sufficient attenuation was observed for the majority of patients when using virtual monoenergetic images (SCM: 96-100% of patients, RCM: 88-94%) without statistical difference between both groups. Segments with attenuation measurements below the threshold remained qualitatively assessable as well. Likert scores were 'excellent' for virtual monoenergetic images 50 keV and 55 keV in both groups (RCM: 1.2-1.4, SCM: 1.2-1.3). With diagnostic image quality maintained, it can be concluded that reduced iodine loads of 14-24.5 gI are feasible for pre-TAVI vascular assessment on a spectral detector CT scanner.